基于贝叶斯网络的个体隐性知识测度方法研究  

Research on individual tacit knowledge measurement method based on Bayesian network

作　　者：陈友玲[1] 张岳园 凌磊 高然 Chen Youling;Zhang Yueyuan;Ling Lei;Gao Ran(State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing 400044,China)

机构地区：[1]重庆大学机械传动国家重点实验室

出　　处：《计算机应用研究》2019年第6期1673-1678,共6页Application Research of Computers

基　　金：国家自然科学基金资助项目(71271224)

摘　　要：隐性知识是企业创新力和竞争力的源泉,因此对其进行合理的表达、转换、分享和测度,将会为企业带来无形却巨大的经济效益。基于此,提出基于贝叶斯网络的个体隐性知识测度方法,构建了包括特定情境的设定和分析,贝叶斯网络拓扑结构的构建、节点概率的参数学习,贝叶斯网络的概率推理、排序和解释及其模型的有效性测试等在内的个体隐性知识测度模型。最后,以L企业研发部的招聘活动为例进行算例分析。结果表明该算例模型的有效性约为75%~80%,验证了所提方法对隐性知识的客观量化测度具有较好的可行性和有效性。Tacit knowledge is the source of innovation and competitiveness of enterprises, so the reasonable expression, transformation, sharing and measurement of it will bring intangible economic benefits to enterprises. Therefore, this paper proposed a method for measuring the individual tacit knowledge based on Bayesian network(BN). Construction of tacit knowledge mea-sure model included: set and analysis of specific situations, Bayesian’s construction of network topology, network node parameter learning Bayes probability, probability inference, sorting and interpretation of the BN, the validity of the model test. Finally, taking L-company's R&D department as an example, the results show that the validity of the model is 75%~80%, which validates the feasibility and effectiveness of the proposed method.

关 键 词：隐性知识 测度方法 贝叶斯网络 个体 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]